Magnetic resonance imaging (MRI) is a medical imaging modality that can create images of the inside of a human body without using x-rays or other ionizing radiation. MRI uses a powerful magnet to create a strong, uniform, static magnetic field (i.e., the “main magnetic field”). When a human body, or part of a human body, is placed in the main magnetic field, the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized. This means that the magnetic moments that are associated with these spins become preferentially aligned along the direction of the main magnetic field, resulting in a small net tissue magnetization along that axis (the “z axis”, by convention). A MRI system also comprises components called gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them. Typically, gradient coils are designed to produce a magnetic field component that is aligned along the z axis (i.e., the “longitudinal axis”), and that varies linearly in amplitude with position along one of the x, y or z axes. The effect of a gradient coil is to create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis. Three gradient coils with orthogonal axes are used to “spatially encode” the MR signal by creating a signature resonance frequency at each location in the body. Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei. These coils are used to add energy to the nuclear spin system in a controlled fashion. As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal. This signal is detected by the MRI system and is transformed into an image using a computer and known reconstruction algorithms.
In certain clinical imaging applications, it is desirable to acquire “diffusion-weighted” images in which tissues that have either higher or lower water self-diffusion characteristics relative to other tissues are emphasized. Typically, diffusion-weighting is implemented using a pair of large gradient pulses bracketing a refocusing RF pulse. Because spins undergoing irregular motion due to diffusion are not completely re-phased by the second gradient pulse of the pair, signal from these spins is attenuated such that tissues with higher water diffusion experience increased signal loss.
Most clinical diffusion-weighted imaging is performed using single-shot sequences, such as single-shot echo-planar imaging (EPI). However, single-shot acquisitions typically have limited resolution and are sensitive to susceptibility-induced image distortions and eddy-current effects. For multi-shot acquisitions, non-diffusive bulk motions can cause shot-specific phase shifts that can destructively interfere when the multiple shots are combined, resulting in serious image artifacts. To reduce image artifacts, these phase shifts may be corrected for each shot individually before combining shots into a final image. Multiple approaches to performing such a motion correction for multi-shot acquisitions are known in the art.
“Parallel imaging” techniques may be combined with multi-shot acquisitions, in which k-space is “under-sampled” (i.e., the Nyquist criteria is not met) and the signals from multiple receiver coils are combined to provide aliasing-free images. Parallel imaging techniques (also known as “partially parallel imaging” techniques) use the spatial sensitivity profiles of the individual receiver coils in addition to traditional gradient encoding to localize the received MRI signals to individual voxels from a source volume of interest. Parallel imaging has been proven successful in reducing scan time for many applications and has also found application in reducing image blurring and geometric distortions in pulse sequences that use long echo trains.
Two families of parallel imaging techniques are known in the art for generating images from incompletely sampled data, based either on the SENSE technique (SENSitivity Encoding), or on the SMASH technique (SiMultaneous Acquisition of Spatial Harmonics). The SENSE-based techniques separately transform the undersampled individual receiver coil k-space data sets into image-space, resulting in spatially aliased images. The aliased images are then combined using weights constructed from measured spatial sensitivity profiles from the individual receiver coils to give a final image with the aliasing artifacts removed.
The first SMASH-based techniques that were developed also used measured spatial sensitivity profiles. These measured spatial sensitivity profiles were used to determine mathematical relationships between neighboring k-space lines in order to synthesize unacquired k-space lines from acquired lines. More recently, autocalibrated imaging (ACI) techniques based on SMASH such as AUTO-SMASH, VD-AUTO-SMASH, and GRAPPA, have been developed that do not require a separate acquisition of data to characterize the spatial sensitivity profiles of the individual receiver coils. Instead, a small region in k-space is acquired with full Nyquist sampling as part of an overall undersampled acquisition. The fully sampled region in k-space is used to determine coefficients that may be used on the unacquired data in k-space to be synthesized from the acquired data. The extra data obtained in the fully sampled region are referred to as “autocalibration data,” and the region of k-space that is fully sampled is the “autocalibration region”.
Techniques are known for correcting motion-induced phase shifts in multi-shot diffusion-weighted acquisitions combined with SENSE-based parallel imaging. However, these techniques are not compatible with multi-shot acquisitions that are combined with autocalibrated SMASH-based parallel imaging. Accordingly, it would be desirable to provide a method for correcting shot-specific phase shifts from non-diffusive bulk motion in autocalibrated, multi-shot diffusion-weighted data.